Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting intermediate reinforcement learning goals. One of the methods includes obtaining a plurality of demonstration sequences, each of the demonstration sequences being a sequence of images of an environment while a respective instance of a reinforcement learning task is being performed; for each demonstration sequence, processing each image in the demonstration sequence through an image processing neural network to determine feature values for a respective set of features for the image; determining, from the demonstration sequences, a partitioning of the reinforcement learning task into a plurality of subtasks, wherein each image in each demonstration sequence is assigned to a respective subtask of the plurality of subtasks; and determining, from the feature values for the images in the demonstration sequences, a respective set of discriminative features for each of the plurality of subtasks.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting intermediate reinforcement learning goals. One of the methods includes obtaining a plurality of demonstration sequences, each of the demonstration sequences being a sequence of images of an environment while a respective instance of a reinforcement learning task is being performed; for each demonstration sequence, processing each image in the demonstration sequence through an image processing neural network to determine feature values for a respective set of features for the image; determining, from the demonstration sequences, a partitioning of the reinforcement learning task into a plurality of subtasks, wherein each image in each demonstration sequence is assigned to a respective subtask of the plurality of subtasks; and determining, from the feature values for the images in the demonstration sequences, a respective set of discriminative features for each of the plurality of subtasks.

Systems and methods are described herein for processing electronic medical images to optimize a review order of pathology cases. For example, a plurality of variables and one or more constraints may be received along with a plurality of pathology cases. Each case of the plurality of pathology cases may include one or more medical images of at least one pathology specimen associated with a patient. The medical images from each case, the plurality of variables, and the one or more constraints may be provided as input to a trained system. A sequential order for user review of the plurality of cases to optimize one or more of the plurality of variables based on the one or more constraints may be received as output of the trained system. Each case of the plurality of cases may be automatically provided to a user for review according to the sequential order.

A computing system can include one or more machine-learned models configured to receive context data that describes one or more entities to be named. In response to receipt of the context data, the machine-learned model(s) can generate output data that describes one or more names for the entity or entities described by the context data. The computing system can be configured to perform operations including inputting the context data into the machine-learned model(s). The operations can include receiving, as an output of the machine-learned model(s), the output data that describes the name(s) for the entity or entities described by the context data. The operations can include storing at least one name described by the output data.

A computing system can include one or more machine-learned models configured to receive context data that describes one or more entities to be named. In response to receipt of the context data, the machine-learned model(s) can generate output data that describes one or more names for the entity or entities described by the context data. The computing system can be configured to perform operations including inputting the context data into the machine-learned model(s). The operations can include receiving, as an output of the machine-learned model(s), the output data that describes the name(s) for the entity or entities described by the context data. The operations can include storing at least one name described by the output data.

The present disclosure provides a sample classification method and apparatus, an electronic device and a storage medium, and relate to the technical field of data mining, in particular to the field of machine learning. The method includes that: a sample to be classified is acquired, and a sample feature dimension of the sample to be classified is greater than a preset threshold; feature encoding is performed on a sample feature of the sample to be classified according to various feature encoding modes to obtain multiple feature vectors; and clustering analysis is performed on the multiple feature vectors to determine a target class of the sample to be classified.

An anomaly detection apparatus generates pieces of image data using a generator and train the generator and a discriminator that discriminates whether an image data, generated by the generator, is real or fake. The anomaly detection apparatus trains the generator such that the generator, in generating the pieces of image data to maximize the discrimination error of the discriminator, generate at least a piece of specified image data to reduce the discrimination error at a fixed rate with respect to the pieces of image data and trains, based on the pieces of image data and the at least a piece of specified image data, the discriminator to minimize the discrimination error.

A method, system and computer-readable storage medium for performing a cognitive information processing operation. The cognitive information processing operation includes: receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via an augmented intelligence system, the augmented intelligence system executing on a hardware processor of an information processing system, the augmented intelligence system and the information processing system providing a cognitive computing function; performing an impartiality assessment operation via an impartiality assessment engine, the impartiality assessment operation detecting a presence of bias in an outcome of the cognitive computing function, the impartiality assessment operation generating a burden score representing the presence of bias in the outcome; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.

A system, article, and method to provide facial expression recognition using linear relationships within landmark subsets.

The present disclosure relates to unsupervised visual attribute transfer through reconfigurable image translation. One aspect of the present disclosure provides a system for learning the transfer of visual attributes, including an encoder, converter and generator. The encoder encodes an original source image to generate a plurality of attribute values that specify the original source image, and to encode an original reference image to generate a plurality of attribute values that specify the original reference image. The converter replaces at least one attribute value of an attribute that is target attribute of the attribute values of the original source image with at least one corresponding attribute value of the original reference image, to obtain a plurality of attribute values that specify a target image of interest. The generator generates a target image based on the attribute values of the target image of interest.